功能辅助机器学习用于预测二元半导体的带间隙
Sitong Huo1, Shuqing Zhang1, Qilin Wu1
1Institute of Information Photonics Technology, School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing 100124, China.
Nanomaterials (Basel, Switzerland)
|March 12, 2024
概括
预测半导体带间隙对于光电子学至关重要. 这项研究结合了机器学习和SISSO方法,以创建只使用基本特征的准确,可解释的模型,突出了电子负性.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算化学计算化学
背景情况:
- 带间隔是半导体光电子设备的关键参数.
- 准确且具有成本效益的频段差距预测仍然是一个挑战.
- 现有的机器学习模型往往缺乏可解释性和物理接地.
研究的目的:
- 开发一种可解释且准确的机器学习模型,用于预测二进制半导体带间隙.
- 将传统的机器学习算法与确定独立性选和分散操作员 (SISSO) 方法集成.
- 通过利用内在的基本特征来降低计算需求.
主要方法:
- 采用混合方法,将支持向量回归 (SVR),随机森林 (RF) 和梯度增强决策树 (GBDT) 与SISSO方法相结合.
- 使用了内在的元素特征和计算的Perdew-Burke-Ernzerhof (PBE) 带间隙.
- 应用该模型来预测1208个理论上稳定的二进制化合物的带间隙.
主要成果:
- 在对二元半导体的带隙预测中实现了更高的准确性和可解释性.
- 该模型确定了电子阴性作为影响材料带间隙的关键因素.
- 成功预测了对大量二进制化合物的数据集的带间隙.
结论:
- 开发的混合ML-SISSO模型为带隙预测提供了一个计算效率高,可解释的方法.
- 这些发现加深了对半导体带间隙的物理原理的理解.
- 这种方法可以指导新型半导体材料的发现和合成.
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